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This thesis is focused on SAS Enterprise Intelligence Platform product and it's capabilities to integrate within a Business Intelligence solution. The aim of the thesis is to describe integration technolgies that the platform features, as well as to determine their application field and compare them, eventually add usage examples. The first part of the thesis explains the general concept and architecture of Business Intelligence, afterwards the reader gets familiar with the SAS Enterprise Intelligence Platform, it's stucture and components and it's Business Intelligence market position. The second part of the thesis contains description (how they work and what they are good for) of the integration technologies available in SAS Enterprise Intelligence Platform. Those technologies are mostly implementations of the most common industry standards (database APIs, COM/DCOM, CORBA, web services). The study also includes description of these standards supported by SAS. The thesis features also comparisions of the integration technologies, namely comparison of database API implementations and comparision of Integrated Object Model API with SAS BI Web Services.

For the centuries, the old Silk routes in hinterland Eurasia were connecting the Eastern and Western empires and served as a channel for exchange of goods and ideas between the two different worlds. Over the past decades, the international trade between Europe and Asia has rapidly increased and reflected more than 10 million TEU transported by container ships annually. As far as the silk, spice and other traditional goods have been gradually replaced by laptops, tablets and car components, the new transport solutions have been tested to meet the altering nature of logistic demands. Nowadays, the company DB Schenker Rail is the European leader in the rail freight market and needs to sustain the leading position. However, DB Schenker Rail attempts to be not only the best choice on European rail, but further with the first pioneer back and forth rail freight activities between Europe and Asia tries to capitalize on the new business opportunities. On that account the company has raised the question: What are the rail freight customer needs today and what he will need tomorrow in relation to the logistic processes between Europe and Asia? As a result, several customer tailored logistic solutions have been described to meet the altering nature of rail freight demands.

This diploma thesis aims at design and implementation of I:ZI Repository application. I:ZI Repository application provides management of data mining tasks and theirs results repository and functions for search in this repository. I:ZI Repository is a REST API build on top of Java EE technology, Berkeley XML database is used for storing data mining tasks. I:ZI Repository application was created based on XQuery search application. The application has completely new structure compared to XQuery search application, all functionality of XQuery search application is present in I:ZI Repository application. Possibilities of using more general search query was added into I:ZI Repository application as well as fuzzy approaches for searching and possibility of clustering search results. Enhanced logging of application activities aimed at logging incoming search queries and outgoing search results is a part of implementation. Results of application testing are included as well.

The goal of this work was to research the available database systems and propose a suitable database system for data archiving. Prepare the communication and processing of data between the database and delivered application Profisignal which processes the measured data from all sensors. Design architecture for the database, the storage of measured data and calculation reports on that data. At the end design the web user interface. The result of my work demonstrate possible ways of archiving data in ORACLE database, examples of calculation the reports and sample of web application, that presented the measured data.

Increasing concern about global climate change has led to a growing interest in energy usage and water consumption. It is well known that changes in consumption habits lead to more efficient use of energy and water sources. Nowadays, globalization, environmental concerns, and the shortage of resources have led to an increase of stakeholder pressure on companies to expand their focus to sustainability. Also, the high impact that the savings can have in the financial status of the company. It is encouraging the headboards to study and improve the ways water and energy are being used within the processes. Significant economic savings and benefits for the environment could be achieved with slight changes in the company. As an overview, this project starts with the extraction of data from a platform for energy management in an industrial company. Then, it goes through the understanding of the energy and water usage data set. Later, a methodology to handle and process the data will be set. It is intending to extract relevant information using clustering. The idea is to compare the usage profiles between different factories, using key performance indicators and reducing the initial data set. Once the benchmarking is performed, some critical parameters will be selected to support the decision-making process related to investments to reduce the energy usage and water consumption in a specific location. Finally, the case of study will be implemented with the measurements from Alfa Laval. We will study how, from daily measurements with a very low investment and using the proper algorithms and methodologies, the main behaviours and features in an industrial location can be extracted from the utilization data. These characteristics can be used to develop strategies or productions schemes based on the interests of the energy manager and the company.

Les base de données (BD) d'Occupation du Sol (OCS) sont d'une grande utilité, dans divers domaines. Les utilisateurs recherchent des niveaux de détails tant géométriques que sémantiques très fins. Ainsi, une telle BD d'OCS à Grande Échelle (OCS-GE) est en cours de constitution à l'IGN. Cependant, pour répondre aux besoins des utilisateurs, cette BD doit être mise à jour le plus régulièrement possible, avec une notion de millésime. Ainsi, des méthodes automatiques de mise à jour doivent être mises en place, afin de traiter rapidement des zones étendues. Par ailleurs, les satellites d'observation de la terre ont fait leurs preuves dans l'aide à la constitution de BD d'OCS à des échelles comparables à celle de CLC. Avec l'arrivée de nouveaux capteurs THR, comme celle du satellite Pléiades, la question de la pertinence de ces images pour la mise à jour de BD d'OCS-GE se pose naturellement. Ainsi, l'objet de cette thèse est de développer une méthode automatique de mise à jour de BDs d'OCS-GE, à partir d'une image satellite THR monoscopique (afin de réduire les coûts d'acquisition), tout en garantissant la robustesse des changements détectés. Le cœur de la méthode est un algorithme d'apprentissage supervisés multi-niveaux appelé MLMOL, qui permet de prendre en compte au mieux les apparences, éventuellement multiples, de chaque thème de la BD. Cet algorithme, complètement indépendant du choix du classifieur et des attributs extraits de l'image, peut être appliqué sur des jeux de données très variés. De plus, la multiplication de classifications permet d'améliorer la robustesse de la méthode, en particulier sur des thèmes ayant des apparences multiples (e,g,. champs labourés ou non, bâtiments de type maison ou hangar industriel, ...). De plus, l'algorithme d'apprentissage est intégré dans une chaîne de traitements (LUPIN) capable, d'une part de s'adapter automatiquement aux différents thèmes de la BD pouvant exister et, d'autre part, d'être robuste à l'existence de thèmes in-homogènes. Par suite, la méthode est appliquée avec succès à une image Pléiades, sur une zone à proximité de Tarbes (65) couverte par la BD OCS-GE constituée par IGN. Les résultats obtenus montrent l'apport des images Pléiades tant en terme de résolution sub-métrique que de dynamique spectrale. D'autre part, la méthode proposée permet de fournir des indicateurs pertinents de changements sur la zone. Nous montrons par ailleurs que notre méthode peut fournir une aide précieuse à la constitution de BD d'OCS issues de la fusion de différentes BDs. En effet, notre méthode a la capacité de prise de décisions lorsque la fusion de BDs génère des zones de recouvrement, phénomène courant notamment lorsque les données proviennent de différentes sources, avec leur propre spécification. De plus, notre méthode permet également de compléter d'éventuels lacunes dans la zone de couverture de la BD générée, mais aussi d'étendre cette couverture sur l'emprise d'une image couvrant une étendue plus large. Enfin, la chaîne de traitements LUPIN est appliquée à différents jeux de données de télédétection afin de valider sa polyvalence et de juger de la pertinence de ces données. Les résultats montrent sa capacité d'adaptation aux données de différentes résolutions utilisées (Pléiades à 0,5m, SPOT 6 à 1,5m et RapidEye à 5m), ainsi que sa capacité à utiliser les points forts des différents capteurs, comme par exemple le canal red-edge de RapidEye pour la discrimination du thème forêts, le bon compromis de résolution que fournit SPOT 6 pour le thème zones bâties et l'apport de la THR de Pléiades pour discriminer des thèmes précis comme les routes ou les haies. / Land-Cover geospatial databases (LC-BDs) are mandatory inputs for various purposes such as for natural resources monitoring land planning, and public policies management. To improve this monitoring, users look for both better geometric, and better semantic levels of detail. To fulfill such requirements, a large-scale LC-DB is being established at the French National Mapping Agency (IGN). However, to meet the users needs, this DB must be updated as regularly as possible while keeping the initial accuracies. Consequently, automatic updating methods should be set up in order to allow such large-scale computation. Furthermore, Earth observation satellites have been successfully used to the constitution of LC-DB at various scales such as Corine Land Cover (CLC). Nowadays, very high resolution (VHR) sensors, such as Pléiades satellite, allow to product large-scale LC-DB. Consequently, the purpose of this thesis is to propose an automatic updating method of such large-scale LC-DB from VHR monoscopic satellite image (to limit acquisition costs) while ensuring the robustness of the detected changes. Our proposed method is based on a multilevel supervised learning algorithm MLMOL, which allows to best take into account the possibly multiple appearances of each DB classes. This algorithm can be applied to various images and DB data sets, independently of the classifier, and the attributes extracted from the input image. Moreover, the classifications stacking improves the robustness of the method, especially on classes having multiple appearances (e.g., plowed or not plowed fields, stand-alone houses or industrial warehouse buildings, ...). In addition, the learning algorithm is integrated into a processing chain (LUPIN) allowing, first to automatically fit to the different existing DB themes and, secondly, to be robust to in-homogeneous areas. As a result, the method is successfully applied to a Pleiades image on an area near Tarbes (southern France) covered by the IGN large-scale LC-DB. Results show the contribution of Pleiades images (in terms of sub-meter resolution and spectral dynamics). Indeed, thanks to the texture and shape attributes (morphological profiles, SFS, ...), VHR satellite images give good classification results, even on classes such as roads, and buildings that usually require specific methods. Moreover, the proposed method provides relevant change indicators in the area. In addition, our method provides a significant support for the creation of LC-DB obtain by merging several existing DBs. Indeed, our method allows to take a decision when the fusion of initials DBs generates overlapping areas, particularly when such DBs come from different sources with their own specification. In addition, our method allows to fill potential gaps in the coverage of such generating DB, but also to extend the data to the coverage of a larger image. Finally, the proposed workflow is applied to different remote sensing data sets in order to assess its versatility and the relevance of such data. Results show that our method is able to deal with such different spatial resolutions data sets (Pléiades at 0.5 m, SPOT 6 at 1.5 m and RapidEye at 5 m), and to take into account the strengths of each sensor, e.g., the RapidEye red-edge channel for discrimination theme forest, the good balance of the SPOT~6 resolution for built-up areas classes and the capability of VHR of Pléiades images to discriminate objects of small spatial extent such as roads or hedge.

This diploma thesis deals with the design and implementation of a framework for the database persistence layer development. This framework is easy to use while keeping the code elegance. It supports object oriented programming features such as inheritance and collections. Other features include versioning of objects and lazy loading. The object metadata are obtained through reflection provided by the .NET framework. The framework is not using any literal for identification (classes, attributes) even in object queries. Most of checks are done by compiler.

This dissertation presents the design of a novel joining-in-type humanoid robot-assisted language learning that uses two robots to conduct a goal-oriented conversation with the human learner to practice English as a second language. The system uses implicit learning as the main learning style to teach the usage of a specific expression form. A mix of tutoring and peer learning is implemented in the course of a three-party conversation. This learning style enables the learner to gain linguistic knowledge, and at the same time it improves the performance of the speech recognition engine. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

With the increasing demand for renewable energy, solar cells have become a crucial technology in meeting this need. Optimizing the placement of solar cells is essential and can be achieved by tracking their performance data. This project's goal was to develop an interface for collecting primarily solar cell-related data, although it is versatile enough to handle any type of data. The report details the sensors and components used in the standard interface. The final product is not strictly a transmitter or receiver, but includes a piece of breadboard allowing for the implementation of various circuits. Additionally, the practical aspects of data collection using a Raspberry Pi and database services are discussed. The completed system features transmitter and receiver circuits, which can be expanded into a larger mesh network as needed.